Download PDFOpen PDF in browserEnd-to-end automated cache-timing attack driven by Machine Learning16 pages•Published: September 6, 2019AbstractCache timing attacks are serious security threats that exploit cache memories to steal secret information.We believe that the identification of a sequence of operations from a set of cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of function calls from cache-timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Contrary to most research, we did not need human processing of the traces to retrieve relevant information. Keyphrases: cache timing attack, deep learning, ecdsa, flush+flush, machine learning, openssl In: Karine Heydemann, Ulrich Kühne and Letitia Li (editors). Proceedings of 8th International Workshop on Security Proofs for Embedded Systems, vol 11, pages 1-16.
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